Longitude and Latitude Prediction Using ARIMA

Authors

  • Vina Ayumi  Faculty of Computer Science, Universitas Mercu Buana, Jakarta, Indonesia

DOI:

https://doi.org//10.32628/CSEIT195617

Keywords:

ARIMA, Neural Network, Genetic Algorithm, Human Mobility

Abstract

As an initial research in human mobility, human mobility prediction can be done by using a time-series predictor algorithm, one of which is ARIMA. ARIMA is short of the integrated moving-average autoregressive. The order of the ARIMA model is represented by the ARIMA symbol (p, d, q), where p is the order of the autoregressive part, d is the order of differencing and q is the order of moving-average process. The research regarding the application of human mobility conducted through five phases, including data collection, data pre-processing, data model building, data prediction and data evaluation. We conducted three times of experiments with different parameters. We defined different value for D, Seasonality, MALags, SMALags and Variance. Based on experiment as conclusion of this research obtained that the best parameter values to get the best MAPE Longitude and MAPE Latitude are Constant = 0, D= 1, Seasonality= 12, MALags = 1, SMALags=12 with MAPE Lon: 0.037433% and MAPE Lat: 0.11632%

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-12-30

Issue

Volume 5, Issue 6, November-December-2019

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Vina Ayumi, " Longitude and Latitude Prediction Using ARIMA, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 6, pp.57-61, November-December-2019. Available at doi : https://doi.org/10.32628/CSEIT195617